Distinguishing experiments for timed nondeterministic finite state machine

The problem of constructing distinguishing experiments is a fundamental problem in the area of finite state machines (FSMs), especially for FSM-based testing. In this paper, the problem is studied for timed nondeterministic FSMs (TFSMs) with output delays. Given two TFSMs, we derive the TFSM intersection of these machines and show that the machines can be distinguished using an appropriate (untimed) FSM abstraction of the TFSM intersection. The FSM abstraction is derived by constructing appropriate partitions for the input and output time domains of the TFSM intersection. Using the obtained abstraction, a traditional FSM-based preset algorithm can be used for deriving a separating sequence for the given TFSMs if these machines are separable. Moreover, as sometimes two non-separable TFSMs can still be distinguished by an adaptive experiment, based on the FSM abstraction we present an algorithm for deriving an r-distinguishing TFSM that represents a corresponding adaptive experiment

Seismic cycles, size of the largest events, and the avalanche size distribution in a model of seismicity

We address several questions on the behavior of a numerical model recently introduced to study seismic phenomena, that includes relaxation in the plates as a key ingredient. We make an analysis of the scaling of the largest events with system size, and show that when parameters are appropriately interpreted, the typical size of the largest events scale as the system size, without the necessity to tune any parameter. Secondly, we show that the temporal activity in the model is inherently non-stationary, and obtain from here justification and support for the concept of a "seismic cycle" in the temporal evolution of seismic activity. Finally, we ask for the reasons that make the model display a realistic value of the decaying exponent $b$ in the Gutenberg-Richter law for the avalanche size distribution. We explain why relaxation induces a systematic increase of $b$ from its value $b\simeq 0.4$ observed in the absence of relaxation. However, we have not been able to justify the actual robustness of the model in displaying a consistent $b$ value around the experimentally observed value $b\simeq 1$.Comment: 11 pages, 10 figure

A bayesian classifier for the recognition of the impersonal occurrences of the 'it' pronoun

International audienceThis paper presents a new system that makes the distinction between the impersonal and anaphoric occurrences of the 'it' pronoun. Compared with the state of the art methods, our system relies on the same types of linguistic knowledge but performs better. We argue that this is due to the bayesian model on which it is based: it enables to combine various pieces of knowledge and to exploit even unreliable ones in the process of pronoun occurrence classification

Testing from a stochastic timed system with a fault model

In this paper we present a method for testing a system against a non-deterministic stochastic finite state machine. As usual, we assume that the functional behaviour of the system under test (SUT) is deterministic but we allow the timing to be non-deterministic. We extend the state counting method of deriving tests, adapting it to the presence of temporal requirements represented by means of random variables. The notion of conformance is introduced using an implementation relation considering temporal aspects and the limitations imposed by a black-box framework. We propose an algorithm for generating a test suite that determines the conformance of a deterministic SUT with respect to a non-deterministic specification. We show how previous work on testing from stochastic systems can be encoded into the framework presented in this paper as an instantiation of our parameterized implementation relation. In this setting, we use a notion of conformance up to a given confidence level

Three Essays on Online Economic Experiments and Experimental Data Analysis

This dissertation consists of three chapters on economic experiments and experimental data analysis. The first two chapters are online experiments and surveys, which explore the two topics of the health state valuation and the voluntary provision of public goods, respectively. The third chapter is a strategy analysis of trust behavior. In the first chapter, to explore how people value the state of health and what socio-economic factors they might consider, I conducted a survey experiment to elicit individuals’ decisions under hypothetical health states. The main task for the subjects was a valuation task (standard gamble) under given health states, in which the subjects were required to make decisions on whether to take a risky medical treatment when facing various success probabilities. After this procedure, the subjects filled a survey about what factors they might have considered when making the previous decisions. The subjects were from two separate online pools of the United States (Amazon MTurk) and Canada (AskingCanadians). My results show that in those who choose the risky medical treatment under the same health states, the Canadian participants are willing to accept a lower success probability. Among the socio-economic factors that are significant to this health valuation, several factors are considered by both samples such as “employer-purchased insurance plans”, “personal financial situations”, and “waiting times for treatment”. Some factors are only significant in one country’s participants. For the American sample, it is “access to health insurance”, while for the Canadian sample, it is “disturbances in everyday family life”. The second chapter is an online experiment of a public goods game, which has a particular feature of polarized preference. From the 2020 U.S. election to the oil pipeline development in Canada, these types of situations may be modeled by a public goods game in which two groups of individuals have polarized preferences. The outcome of the election or debate will affect the utility of individuals in both groups but in an opposite direction. Meanwhile, individuals from each group can make costly efforts (in their favor) trying to affect the outcome. We study a public goods game with polarized preferences by using a generalized voluntary contribution mechanism (GVCM). The strategy method was applied to the design of an online experiment. There are two groups of players, a majority group and a minority group, who have polarized preferences for a public good. Each player decides whether to contribute to their group's public account or keep the token in their private account. The experiment consists of a 2×2 design, which allows us to examine the effect of different MPCRs and frameworks in the conditional contribution. The subjects were recruited from Amazon MTurk and the experiment was implemented using o-Tree. The main results that we found are that the MPCR effect and framework effect are mixed and only significant in some treatments. The results vary depending on the role of the participants (the majority and the minority). Overall, the individual contribution frequency in the majority group is significantly larger than in the minority group. Furthermore, players' contribution significantly increases with the contribution of others in their own group but is not dependent on the contribution from the other group. The third chapter is an experimental data analysis, which seeks to reveal the strategy behind trust behavior among the encountering of strangers. The data set is from a trust game experiment reported by Duffy, Xie, and Lee (2013). If people never met again and people would not be punished for dishonesty (at least not directly from the person they cheated), the actions would be different. This scenario could be simulated in a trust game where there are two roles (Investor and Trustee) and the subjects are randomly and anonymously matched. The method of finite automata is applied to infer the strategies subjects used in the experiment. In the strategy fitting procedure, I define for Investor 16 strategies and 6 strategy sets, and for the second player (Player B: Trustee) 24 strategies and 11 strategy sets. Then I match the data through a fitting procedure with these defined strategies. I report that the top three strategies in order are “grim trigger”, “systematically Send”, and “forgiving”; for Trustee, the most used strategies are “systematically Return”, “grim trigger”, and “tit-for-tat”. By taking the probability of the participant’s error into account, more observations are classified into the strategy, and the strategy pattern and proportions are still maintained

A Markov-Based Intrusion Tolerance Finite Automaton

It is inevitable for networks to be invaded during operation. The intrusion tolerance technology comes into being to enable invaded networks to provide the necessary network services. This paper introduces an automatic learning mechanism of the intrusion tolerance system to update network security strategy, and derives an intrusion tolerance finite automaton model from an existing intrusion tolerance model. The proposed model was quantified by the Markov theory to compute the stable probability of each state. The calculated stable probabilities provide the theoretical guidance and basis for administrators to better safeguard network security. Verification results show that it is feasible, effective, and convenient to integrate the Markov model to the intrusion tolerance finite automaton

USING HOMING, SYNCHRONIZING AND DISTINGUISHING INPUT SEQUENCES FOR THE ANALYSIS OF REVERSIBLE FINITE STATE MACHINES

A digital device is called reversible if it realizes a reversible mapping, i.e., the one for which there exist a unique inverse. The field of reversible computing is devoted to studying all aspects of using and designing reversible devices. During last 15 years this field has been developing very intensively due to its applications in quantumcomputing, nanotechnology and reducing power consumption of digital devices. We present an analysis of the Reversible Finite State Machines (RFSM) with respect to three well known sequences used in the testability analysis of the classical Finite State Machines (FSM). The homing, distinguishing and synchronizing sequences areapplied to two types of reversible FSMs: the converging FSM (CRFSM) and the nonconverging FSM (NCRFSM) and the effect is studied and analyzed. We show that while only certain classical FSMs possess all three sequences, CRFSMs and NCRFSMs have properties allowing to directly determine what type of sequences these machines possess

UML models consistency management: guidelines for software quality manager

Unified Modeling Language (UML) has become the de-facto standard to design today’s large-size object-oriented systems. However, focusing on multiple UML diagrams is a main cause of breaching the consistency problem, which ultimately reduces the overall software model’s quality. Consistency management techniques are widely used to ensure the model consistency by correct model-to-model and model-to-code transformation. Consistency management becomes a promising area of research especially for model-driven architecture. In this paper, we extensively review UML consistency management techniques. The proposed techniques have been classified based on the parameters identified from the research literature. Moreover, we performed a qualitative comparison of consistency management techniques in order to identify current research trends, challenges and research gaps in this field of study. Based on the results, we concluded that researchers have not provided more attention on exploring inter-model and semantic consistency problems. Furthermore, state-of-the-art consistency management techniques mostly focus only on three UML diagrams (i.e., class, sequence and state chart) and the remaining UML diagrams have been overlooked. Consequently, due to this incomplete body of knowledge, researchers are unable to take full advantage of overlooked UML diagrams, which may be otherwise useful to handle the consistency management challenge in an efficient manner